class_docstr: Calculates the R-Squared, the
  `coefficient of determination <https://en.wikipedia.org/wiki/Coefficient_of_determination>`_.
  
  .. math::
      R^2 = 1 - \frac{\sum_{j=1}^n(A_j - P_j)^2}{\sum_{j=1}^n(A_j - \bar{A})^2}
  
  where :math:`A_j` is the ground truth, :math:`P_j` is the predicted value and
  :math:`\bar{A}` is the mean of the ground truth.
  
  - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
  - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`.
  
  Parameters are inherited from ``Metric.__init__``.
  
  Args:
      output_transform: a callable that is used to transform the
          :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
          form expected by the metric. This can be useful if, for example, you have a multi-output model and
          you want to compute the metric with respect to one of the outputs.
          By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
      device: specifies which device updates are accumulated on. Setting the
          metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
          non-blocking. By default, CPU.
  
  Examples:
      To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
      The output of the engine's ``process_function`` needs to be in format of
      ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``.
  
      .. include:: defaults.rst
          :start-after: :orphan:
  
      .. testcode::
  
          metric = R2Score()
          metric.attach(default_evaluator, 'r2')
          y_true = torch.tensor([0., 1., 2., 3., 4., 5.])
          y_pred = y_true * 0.75
          state = default_evaluator.run([[y_pred, y_true]])
          print(state.metrics['r2'])
  
      .. testoutput::
  
          0.8035...
  
  .. versionchanged:: 0.4.3
      Works with DDP.
code: def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
      y_pred, y = output
      self._num_examples += y.shape[0]
      self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).to(
          self._device
      )
  
      self._y_sum += torch.sum(y).to(self._device)
      self._y_sq_sum += torch.sum(torch.pow(y, 2)).to(self._device)
  
file_path: ignite/ignite/contrib/metrics/regression/r2_score.py
imports: from typing import Tuple
  import torch
  from ignite.contrib.metrics.regression._base import _BaseRegression
  from ignite.exceptions import NotComputableError
  from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
label_desc: Use image node_id 2 for calling the R2Score obj's underlying member method code with example usage: obj._update(output) without return types